Scope
This document aims to perform an integrated analysis on all available
cells that passed the QC filtering. Results obtained in this analysis
will determine if multilineage can be observed in any of the conditions.
Samples have been previously been assigned a cell type using SingleR
(Aran et al. 2019) and the (Bone Marrow)
Mouse Cell Atlas (Wang et al. 2022).
Sample integration is required since there is no perfect overlapping
between both samples.
Primary data
A total of two samples (Bone Marrow) were
library-prepared and sequenced by CRG Sequencing platform at PRBB.
Libraries were prepared using 10x Single Cell 3’v3 kit.
Corresponding biological samples were delivered to the sequencing
platform in December’24 and sequencing data was received in February’24.
Each sample corresponds to a different condition:
- CD45+ cells at 4.5 months (CD45_4_5)
- CD45+ cells transplanted from KDR+ progenitors at 4.5 months
(CD45_BFP_4_5) activated from 7* specific genes
identified by CRISPRa in an earlier project phase.
*Genes related to HSC development fate.
Methods
Following criteria is applied:
- SCTransform-based normalization (v2) regressing out mitochondrial
content. Rest of parameters by default.
- Dimensionality reduction considering 35 PCs which corresponds to a
total cumulative variance explained of 85%.
- Clustering (Louvain algorithm) with resolutions explored: from 0.1
to 0.6 (less to more specific).
- ‘FindAllMarkers’ was used to identify differentially expressed genes
for each cluster and condition.
All previous steps and integration are performed by means of Seurat R
package (Aran et al. 2019) (v5.0.0)
together with glmGamPoi R package (Ahlmann-Eltze
and Huber 2021) (v.1.12.2). Check vignette https://satijalab.org/seurat/archive/v4.3/sctransform_v2_vignette
Results
As a reminder, the dataset to be analyzed contains 17396 features
(genes) over 12530 cells distributed in 2 samples from two conditions.
In this case, ribosomal genes were excluded from the dataset. Low
quality cells were also discarded as a result of a previous QC.
The UMAP shows a great overlap between samples after the
integration.
This representation confirms that some cell types are predominant in
the non transplanted cells (Macrophages) and others in the transplanted
cells (B cells and T cells).
It is recommended to visualize UMAP against other possible
confounding variables i.e. mitochondrial content or number of genes
detected per cell. This is shown in next figures:
Cell phase
Finally, cell phase can be inferred from a reference set of genes
defined by (Tirosh et al. 2016) and phase
labels represented in UMAP, as shown in following figure:
Since this dataset includes developing cells, it is not recommended
to regress out this information.
Seurat Annotation
Different clustering resolutions are explored from lower (less
specific cell type) to higher granularity (more specific cell type).
Clusters can be represented over UMAP, the following graph show this
layout for clustering resolution 0.4.
A clustering resolution of 0.4 is chosen. Let’s
visualize the clusters per condition:
The distribution of cells among clusters and between group is:
##
## CD45_4_5 CD45_BFP_4_5
## 0 1370 466
## 1 1008 616
## 2 957 429
## 3 157 1142
## 4 706 430
## 5 657 125
## 6 519 211
## 7 501 92
## 8 378 200
## 9 426 87
## 10 305 141
## 11 181 156
## 12 121 204
## 13 117 183
## 14 9 272
## 15 34 241
## 16 64 25
Subclustering
There are some cell types not present in the Mouse Cell Atlas that
can be observed using different Hematopoietic markers. The manual re
annotation process is as follows: - Plot markers of an specific cell
type and observe positive cells in one seurat cluster (res 0.4) - If all
the cluster is positive for this marker, it is re annotated. - If the
markers are partially expressed, a subcluster is calculated to fit the
positive cells.
MAGIC imputation
MAGIC (Dijk et al. 2018) is an
imputation algorithm used to restore the structure of the data. We will
use this tool to visualize the expression of different hematopoietic
markers. This is an example of how the algorithm works:
The plot on the right shows how the imputation has revealed the
expression of the gene “Gata3” in one cluster.
Hematopoietic markers
From Kucinski et al, 2024: https://doi.org/10.1016/j.stem.2023.12.001
HSC_markers
This signature is expressed in cluster 11, and therefore it has been
tagged as “Hematopoietic stem cell”.
Neutrophil_Progenitor_markers
This signature is expressed in cluster 9, and therefore it has been
tagged as “Neutrophil progenitor”.
Basophil_Progenitor_markers
This signature is expressed partially in cluster 11. After
subclustering, cluster 11_6 fits the positive cells, and therefore it
has been tagged as “Basophil progenitor”.
ILC_markers
This signature is consistently expressed in cluster 14, and therefore
it has been tagged as “Innate lymphoid cell”.
Curated.cell.ident
After re annotation, cell types are represented as follows:
